# -*- coding:utf-8 -*-

import numpy as np
from scipy.optimize import linprog

class FactoryOptimization:
    def __init__(self, months=12):
        self.months = months
        self.prices = np.array([[100, 120, 130, 150, 160],  # g1, g2, g3, g4, g5
                                 [100, 130, 160, 150, 170],  # B工厂
                                 [130, 150, 0, 0, 0]])  # C工厂 (g5 不生产)
        self.costs = np.random.randint(10, 30, (3, 3))  # 随机成本
        self.qualities = np.random.uniform(0.8, 1.0, (3, 3))  # 随机合格率
        self.inputs = np.random.randint(100, 200, (3, 3))  # 随机投入量
        self.base_profit = 1000  # 基线利润
        self.optimal_solution = {}

    def collect_expert_data(self):
        # 模拟专家行为，返回状态-动作对
        return np.random.rand(10, 2)  # 示例数据

    def infer_reward_function(self, expert_data):
        # 简单模型推断奖励函数
        return np.random.rand(len(expert_data))

    def solve_lp(self):
        # 设置目标函数（最大化利润）
        c = -np.array([self.prices[i][j] - self.costs[i][j] for i in range(3) for j in range(3)])  # 目标函数系数

        # 设置约束条件
        A_ub = np.array([[1, 1, 0, 0, 0, 0],   # 约束条件示例
                         [0, 0, 1, 1, 0],
                         [0, 0, 0, 0, 1]])  # 实际条件应根据问题调整

        b_ub = np.array([self.base_profit] * 3)  # 约束条件右侧

        # 求解线性规划
        result = linprog(c, A_ub=A_ub, b_ub=b_ub, bounds=(0, None))
        return result

    def optimize(self):
        # 收集专家数据
        expert_data = self.collect_expert_data()

        # 推断奖励函数
        rewards = self.infer_reward_function(expert_data)

        # 求解线性规划
        lp_solution = self.solve_lp()

        # 存储最优解
        self.optimal_solution = {
            'profit': -lp_solution.fun,
            'allocation': lp_solution.x
        }

    def print_optimal_solution(self):
        print("最优解:")
        print(f"每个月的最大利润: {self.optimal_solution['profit']}")
        print(f"产品分配: {self.optimal_solution['allocation']}")

# 实例化优化类并运行
factory_optimizer = FactoryOptimization()
factory_optimizer.optimize()
factory_optimizer.print_optimal_solution()
